The architectures for the semantic segmentation models used in this paper are the same as those used in the original papers.
We keep the same number of convolution and deconvolution layers for the encoding and decoding stages.
We maintain the same amount of hidden units for each, that is, channels per layer, and we maintain the same non-linear activation functions and hyperparameters.
The setup of the hourglass models is defined in their respective papers for the architectures we used, namely, FCN8 [1]}, ParseNet [2]}, SegNet [3]}, FastNet [4]}, UNet [5]}, DeconvNet [6]}, AdapNet++ [7]}, CGBNet [8]}, FC-DenseNet67 [9]}, and ENet [10]}.
